Pollutant removal and bioelectricity generation from urban river sediment using a macrophyte cathode sediment microbial fuel cell (mSMFC).

Sediment microbial fuel cell (SMFC) efficacy depends highly on organic matter flux and dissolved oxygen (DO) at the anode and cathode, respectively. However, utilizing floating-macrophyte for elevated DO supply at the cathode has not been fully explored. Therefore, a novel floating-macrophyte implanted biocathode single-chamber SMFC (mSMFC) was developed for the simultaneous removal of pollutant and bioelectricity generation from polluted urban river sediment. With Lemna minor L. employed in mSMFC, high pollutant removal was feasible as opposed to the control bioreactor. Total COD, nitrate and sulfate removal reached 57%, 99%, and 99%, respectively. Maximum voltage output, power density, columbic efficiency, normalized energy recovery, and net energy production observed was 0.56 ±â€¯0.26 V, 86.06 mW m-3, 24.7%, 0.033 kWh m-3 and 0.020 kWh m-3, respectively. Alternatively, when floating-macrophyte (predominantly Pistia stratiotes) was employed in the catholyte, DO increased significantly to about 10 mg L-1 in the mSMFC. 16S rRNA gene sequencing revealed Euryarchaeota-(90.91%) and Proteobacteria-(59.68%) as the dominant phyla affiliated to archaea and bacteria, respectively. Pollutant removal mechanisms observed within the mSMFC included bioelectrochemical oxidation at the anode and reduction reaction and macrophyte hyperaccumulation at the cathode. The novel mSMFC system provided an effective approach for the removal of pollutant and bioelectricity generation.

Efficiency of an upflow anaerobic sludge blanket reactor treating potato starch processing wastewater and related process kinetics, functional microbial community and sludge morphology.

Herein, an upflow anaerobic sludge blanket reactor was employed to treat potato starch processing wastewater and the efficacy, kinetics, microbial diversity and morphology of sludge granules were investigated. When organic loading rate (OLR) ranging from 2.70 to 13.27kgCOD/m3.d was implemented with various hydraulic retention times (72h, 48h and 36h), COD removal could reach 92.0-97.7%. Highest COD removal (97.7%) was noticed when OLR was 3.65kgCOD/m3.d, but had declined to 92.0% when OLR was elevated to 13.27kgCOD/m3.d. Methane and biogas production increased from 0.48 to 2.97L/L.d and 0.90 to 4.28L/L.d, respectively. Kinetics and predictions by modified-Gompertz model agreed better with experimental data as opposed to first-order kinetic model. Functional population with highest abundance was Chloroflexi (28.91%) followed by Euryarchaeota (22.13%), Firmicutes (16.7%), Proteobacteria (16.25%) and Bacteroidetes (7.73%). Compared with top sludge, tightly-bound extracellular polymeric substances was high within bottom and middle sludge. Morphology was predominantly Methanosaeta-like cells, Methanosarcina-like cells, rods and cocci colonies.

Functional bacterial and archaeal diversity revealed by 16S rRNA gene pyrosequencing during potato starch processing wastewater treatment in an UASB.

Microbial community structure of sludge sampled from an UASB treating potato starch processing wastewater (PSPW) was investigated. Operational taxonomic units revealed at 97% sequence identity tolerance was 2922, 2869 and 3919 for bottom, middle and top sections of the reactor, respectively. Overall abundant phylum observed within the UASB was low-G+C-Gram-positive bacteria affiliated to Firmicutes (26.01%) followed by Chloroflexi (16.70%), Proteobacteria (12.71%), Cloacimonetes (10.72%), Bacteroidetes (7.87%), Synergistetes (9.02%) and Euryarchaeota (8.82%). Whiles Firmicutes had dominated the bottom and top section by 34.01% and 28.64%, respectively, middle section was predominantly Euryarchaeota (24.32%) with major dominance in methanogens affiliated to genus Methanosaeta. The results demonstrated substantial stratification of the microbial community structure along the reactor height with various functional bacterial groups which subsequently allowed degradation of organics in PSPW in sequential mode. The findings herein would provide guidance for optimizing the anaerobic process and operation of the UASB.

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R2) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model.